Podcasts about ted fellow pratik shah

Today's AI algorithms require tens of thousands of expensive medical images to detect a patient's disease. What if we could drastically reduce the amount of data needed to train an AI, making diagnoses low-cost and more effective? TED Fellow Pratik Shah is working on a clever system to do just that. Using an unorthodox AI approach, Shah has developed a technology that requires as few as 50 images to develop a working algorithm -- and can even use photos taken on doctors' cell phones to provide a diagnosis. Learn more about how this new way to analyze medical information could lead to earlier detection of life-threatening illnesses and bring AI-assisted diagnosis to more health care settings worldwide. Hosted on Acast. See acast.com/privacy for more information.

Today's AI algorithms require tens of thousands of expensive medical images to detect a patient's disease. What if we could drastically reduce the amount of data needed to train an AI, making diagnoses low-cost and more effective? TED Fellow Pratik Shah is working on a clever system to do just that. Using an unorthodox AI approach, Shah has developed a technology that requires as few as 50 images to develop a working algorithm -- and can even use photos taken on doctors' cell phones to provide a diagnosis. Learn more about how this new way to analyze medical information could lead to earlier detection of life-threatening illnesses and bring AI-assisted diagnosis to more health care settings worldwide.

Die heutigen KI-Algorithmen benötigen zehntausende teure medizinische Bilder, um die Krankheit eines Patienten zu entdecken. Was, wenn wir die Menge der Daten zum Training der KI drastisch reduzieren könnten, um so die Diagnosen günstig und viel effektiver zu machen? TED-Fellow Pratik Shah arbeitet an einem schlauen System, um genau das zu machen. Durch die Nutzung einer ungewöhnlichen KI-Methode hat Shah eine Technologie entwickelt, die gerade einmal 50 Bilder benötigt, um einen funktionierenden Algorithmus zu entwickeln -- und auch Bilder der Handykamera eines Arztes nutzen kann, um eine Diagnose zu stellen. Erfahren Sie mehr darüber, wie diese neue Art medizinische Daten zu analysieren zu einer früheren Erkennung lebensbedrohlicher Krankheiten führen könnte und KI-unterstützte Diagnosen zu mehr medizinischen Einrichtungen weltweit bringen könnte.

오늘날 인공지능의 알고리즘으로 환자의 질병을 진단 하려면, 수 천장의 값비싼 의료영상이 필요합니다. 인공지능의 학습을 위해 필요한 자료의 양을 대폭 줄여, 진단비용은 낮추고 효율성은 높인다면 어떨까요? TED Fellow 프라틱 샤(Pratik Shah)는 바로 그 일을 하는 지능적인 시스템을 개발하고 있습니다. 그는 색다른 방식으로 인공지능을 활용해, 단 50장의 의료영상으로 알고리즘을 학습시킬 수 있고, 심지어 의사들의 휴대전화로 찍은 사진만으로도 진단이 가능한 기술의 개발에 성공했습니다. 의료정보의 분석에 쓰이는 이 새로운 방법이, 어떻게 생명을 위협하는 질병을 조기에 진단하고, 인공지능의 활용을 전 세계의 의료 현장에 보급할 수 있는지 알아봅니다.

Los actuales algoritmos de inteligencia artificial necesitan decenas de miles de costosas imágenes para detectar las enfermedades. ¿Y si pudiéramos reducir la cantidad de información que se necesita para entrenar a un equipo de IA para que realice diagnósticos más eficaces y baratos? TED Fellow Pratik Shah trabaja en un ingenioso sistema para lograrlo. Mediante un enfoque no convencional de la IA, Shah ha desarrollado una técnica que requiere tan solo 50 imágenes para desarrollar un algoritmo activo, y que incluso puede usar fotografías tomadas desde el teléfono del médico para indicar el diagnóstico. Aprenda más sobre cómo esta nueva forma de analizar la información médica podría ayudar a detectar más temprano las enfermedades terminales y sobre cómo se podrían llevar los diagnósticos con programas de IA a más centros de salud en todo el mundo.